The invention pertains to the field of liquid chromatography systems (hereafter LC), and, more particularly, to the field of flow stability measurement systems for LC systems.
In LC systems, the proper functioning of the system in identifying the components in a sample and their quantities depends upon a stable flow rate of solvent carrying the sample through the column. In the prior art, flow rate stability systems typically timed the time it took the pump shaft to travel through one revolution and compared the time for each cycle to a constant for the desired flow rate. Typically, such pumps are controlled by control systems which alter the motor speed to maintain a constant desired flow rate. If air bubbles or other problems caused the flow rate to change, this fact would be reflected in a change of the actual pressure of solvent at the head of the LC column. Because typical control systems monitor this pressure and compare it to a target pressure set by a computer to establish the desired flow rate, the changes in actual pressure resulted in non zero error signals which caused the motor speed to be changed to compensate in the direction needed to maintain the constant flow rate. These changes in pump speed would be reflected in the time per revolution. Thus problems like air leaks into the system which caused instability of flow rate could be detected by looking at revolution times.
The problem with these prior art systems is that they are slow to respond and difficult to interpret. Typically, such systems had 100 bits, one of which was assigned to each of the 100 most recent revolutions of the pump shaft. Each time a revolution time differed from a constant defining the time it should have taken at the pertinent flow rate, the bit for that revolution would be set. Typically, when the pump is first turned on, the first few cycles are all "bad" and these first bits would be remembered for the first 100 cycles even though the system is stable. At low flow rates, these first bad cycles would be part of the most recent 100 cycles for up to an hour. Thus, the user could misinterpret the report of say 5 bad cycles as a problem, when in fact the system has stable flow in the steady state. The correct way to interpret such systems was if the number of bad cycles was stable and not growing. Further, such systems usually had thresholds that would cause a report of a problem only if the number of bad cycles exceeded the threshold. Thus mediocre performance that did not exceed the threshold might not be detected if it was stable and not a growing number. Also, a major failure of stability on only one cycle would only be reported as one more bad cycle and no measure of the magnitude of the problem would be presented in the prior art systems.
Thus, there arose a need for a system which could give a faster response which was easier to interpret and which gave a quantitative measure of the magnitude of the flow stability problem.